An Exploration of Online Interactive Teaching Mode of Vocational Undergraduate Business English Based on Deep Learning Networks

Abstract

English teaching is highly dependent on interactive communication, and for a long time, interactive teaching methods and tools have often had difficulty fully showing their advantages in the classroom due to subjective and objective conditions. In this study, based on self-coding networks, KL entropy is introduced for sparse limitation. Then, an adaptive gradient method with dynamic momentum and an essential learning rate is used to optimize the deep self-coding network. At the same time, an online interactive teaching model of vocational undergraduate business English is constructed from the aspects of pre-course preparation, platform selection, and in-class content, and teaching practice is carried out to explore the effect of the model. The results show that the significant difference between the posttest English scores of the experimental class and the control class in University Z is 0.000<0.05, and the mean value of the English scores of the experimental class is slightly higher than the mean value of the English scores of the control class. This study is based on Adamdyn’s online interactive teaching model, which is effective in teaching vocational undergraduate business English.